Machine learning-based prediction of nasopharyngeal carcinoma risk: a clinical approach
This study presents a robust and interpretable ML-based approach for NPC risk prediction, integrating EBV serology and hematological markers. The model demonstrated high predictive accuracy and potential for population-based screening, providing an efficient tool for early NPC detection and intervention planning.
Authors
- Zhiqiang Huang (ORCID: https://orcid.org/0000-0001-7809-3241)
- Wenting Pan
- Wenhui Yang (ORCID: https://orcid.org/0009-0007-1994-7033)
- Hui-pin Huang
- Chengyan Zhou (ORCID: https://orcid.org/0000-0003-2215-3705)
- Haiqing Zhu (ORCID: https://orcid.org/0000-0002-4364-037X)
- Yulin Yuan (ORCID: https://orcid.org/0000-0003-0393-7475)
- Shangyang Li (ORCID: https://orcid.org/0000-0002-5138-6076)
- Minzhong Tang
- Yujuan Liang
Institutions
- The People's Hospital of Guangxi Zhuang Autonomous Region (CN)
- Hegang People's Hospital (CN)
- Wuzhou Red Cross Hospital (CN)
Publication Details
- Journal
- Frontiers in Immunology
- Published
- 2025-11-27
- DOI
- https://doi.org/10.3389/fimmu.2025.1648648
- Citations
- 1
- Primary Topic
- Head and Neck Cancer Studies
- Type
- article
- Field-Weighted Citation Impact
- 1.85